جست‌وجوی استاد و پژوهشگر

• Combinatorics
• Graph Theory
• Optimization on Graphs
• +۳ مورد دیگر

Martí Farré is a faculty member in the Department of Mathematics at the Faculty of Mathematics and Statistics, Universitat Politècnica de Catalunya (UPC). He is actively involved in the COMBGRAPH research group (Combinatorics, Graph Theory, and Applications) and the OMGRAPH subgroup focusing on optimization methods in graphs. His research spans theoretical and applied aspects of discrete mathematics. Research Interests: Martí's work centers on Combinatorics and Graph Theory , with particular emphasis on extremal problems, connectivity, matching theory, independence number, and spectral graph properties. His research often investigates structural and optimization properties of graphs under constraints such as fixed diameter, degree sequences, or matching numbers. The analysis of his recent publications reveals a consistent focus on extremal graph theory, where he derives bounds and characterizes graphs maximizing or minimizing certain parameters (e.g., edges, diameter, connectivity) under given constraints. Key themes include vulnerability and robustness of networks, spectral properties, and combinatorial optimization on graphs. Scientific Awards and Recognition: Premi o reconeixement (Recognition or Prize) Advising and Grants: Martí has contributed to competitive R&D+i projects and innovation initiatives in education. He has supervised doctoral theses, indicating an active role in mentoring graduate students, although specific student names are not listed in the provided data. Labs and Research Teams: He is a key member of the COMBGRAPH research group at UPC, a leading team in combinatorics and graph applications, and participates in the OMGRAPH subgroup dedicated to optimization on graphs, contributing to both theoretical advances and real-world applications.

• Parameterized Complexity
• Algorithms
• Graph Theory
• +۳ مورد دیگر

Saket Saurabh is a Professor at the Department of Informatics, University of Bergen. His research focuses on parameterized complexity, algorithms, graph theory, and combinatorial optimization. He has contributed extensively to theoretical computer science, with a strong emphasis on algorithm design and analysis for NP-hard problems. His work includes studies on graph algorithms, approximation schemes, and fairness in computational problems. Recent publications address topics such as minimum membership dominating sets, hybrid clustering, and fair hitting set problems. Saurabh has collaborated widely, with co-authors like Fedor Fomin, Petr Golovach, and others. Key research interests include parameterized algorithms for graph problems, exponential-time approximation methods, and structural graph theory. His contributions have advanced the understanding of computational complexity and practical algorithmic solutions for challenging problems.

Cheryl Groodمشاهده پروفایل

استاد

• Representation Theory
• Combinatorics
• Graph Theory
• +۲ مورد دیگر

Cheryl Grood is a Professor of Mathematics at Swarthmore College, located in Swarthmore, PA. She holds a B.A. in Honors Mathematics from the University of Michigan (1992) and a Ph.D. in Mathematics from the University of Wisconsin (1998), advised by Georgia Benkart. Her research focuses on representation theory, combinatorics, graph theory, and linear algebra, with recent work exploring intersections between these fields. She has taught a wide range of courses including Calculus, Linear Algebra, Abstract Algebra, and Discrete Mathematics, emphasizing inclusive pedagogy to support underrepresented students in STEM. Grood has contributed to educational reform initiatives across all academic levels and is involved in programs fostering equity in mathematics education. Her teaching record spans over two decades at Swarthmore, with notable roles in STEM Scholars Programs and directed research seminars. She has advised numerous undergraduate research projects in topics such as Lie Algebras, Representation Theory, and Graph Theory. While no explicit awards are noted, her contributions to both research and pedagogy reflect sustained excellence in academia. Grood's research output includes seminal work in partition algebras and rook monoids, as well as recent contributions to graph theory and minimum rank problems. Her courses frequently integrate advanced topics like representation theory of symmetric groups and algebraic structures, reflecting her dual commitment to foundational teaching and cutting-edge research.

• Algorithms
• Data Structures
• Dynamic Graph Algorithms
• +۴ مورد دیگر

Monika Henzinger is a Professor at the Institute of Science and Technology Austria (ISTA) since 2023 and Vice President for Technology Transfer at ISTA since 2024. Her research focuses on efficient algorithms and data structures , particularly in dynamic settings where input data changes continuously. She also investigates privacy-preserving algorithms and the practical implementation of theoretically optimal algorithms. Education: PhD in Computer Science (1993, Princeton University, USA) Research Interests: Her work spans dynamic graph algorithms, differential privacy, and transforming theoretical algorithms into practical solutions. Key areas include minimizing computational resources, proving limits of efficiency, and ensuring data privacy through noise addition. Article Trends: Recent publications emphasize dynamic algorithms for graph problems , differential privacy , and clustering techniques . Topics such as minimum cut, bipartite matching, and edge coloring in dynamic environments are prominent, alongside privacy-preserving data analysis. Scientific Awards: 2024 Best Paper Award (SODA) 2021 Wittgenstein Award and ERC Advanced Grant 2019 Carus Medal (Leopoldina Academy) 2016 ACM Fellow 2014 ERC Advanced Grant 2004 European Young Investigator Award Advising & Grants: She advises PhD students and leads projects funded by grants such as the ERC Advanced Grant, Wittgenstein Award, and Austrian Science Fund initiatives. Her team includes postdocs and PhD candidates working on dynamic data structures and privacy. Labs & Teams: Leads the Henzinger_Monika Group at ISTA, focusing on algorithms, data structures, and dynamic graph problems. Collaborates with institutions globally on projects related to network optimization and privacy.

• Probabilistic Methods and Analysis
• Algorithms
• Mathematical Programming
• +۳ مورد دیگر

Karthekeyan Chandrasekaran is an Associate Professor in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign (UIUC), where he has been since 2014. He holds an affiliate position in the Department of Computer Science. His academic career includes a Visiting Fellowship at ICERM (Spring 2023) and Eötvös Loránd University (Budapest, Fall 2022). He earned a B.Tech. in Computer Science and Engineering from the Indian Institute of Technology Madras (2007) and a Ph.D. in Algorithms, Combinatorics, and Optimization from Georgia Institute of Technology (2012). Before joining UIUC, he was a Simons Postdoctoral Research Fellow at Harvard University (2012–2014). His research focuses on Probabilistic Methods and Analysis , Algorithms , Mathematical Programming , and Combinatorial Optimization . He explores theoretical foundations of optimization, graph theory, and hypergraphs, with applications in algorithm design and complexity analysis. Recent work emphasizes hypergraph partitioning, submodular functions, and approximation algorithms. Chandrasekaran has received the Sharp Outstanding Teaching Award in Industrial Engineering (2018) and the College of Computing Dissertation Prize (2012) . His teaching includes courses such as Deterministic Models in Optimization and Combinatorial Optimization , reflecting his expertise in operations research and algorithmic theory. His research output spans over 50 publications, addressing cutting-edge topics like hypergraph connectivity augmentation, feedback vertex set problems, and strongly polynomial algorithms. He actively contributes to theoretical computer science and combinatorial optimization, with a focus on bridging mathematical rigor and practical algorithmic solutions.

• High-Performance Computing
• Parallel Computing
• Heterogeneous Computing
• +۷ مورد دیگر

Alberto José Gonçalves Carvalho Proença serves as Full Professor in the Department of Informatics at the School of Engineering, University of Minho, and holds the position of Senior Researcher with Dr. habil at Centro ALGORITMI. His academic career spans over 45 years, beginning at the University of Porto in 1976 before transitioning to the University of Minho in 1977 where he has remained ever since. His educational foundation includes: Licentiate in Electrical Engineering from Coimbra, Portugal (1976) MSc in Digital Electronics from UMIST, Manchester, UK (1979) PhD from Manchester, UK (1982) Dr. habil (Habilitation) from the University of Minho (1998) Professor Proença's research spans from digital electronics to cutting-edge high-performance computing, with core expertise in computer architecture, parallel computing, and heterogeneous computing systems. His work bridges theoretical computer science with practical applications across diverse domains including particle physics (LHC data analysis), forensic science (DNA library systems), materials science (crystallographic imaging), and cultural heritage preservation. Current research focuses on optimizing computational efficiency across heterogeneous resources for scientific applications, addressing challenges in big data processing, parallel random number generation, and scheduling algorithms for distributed environments. Throughout his career, Professor Proença has successfully supervised numerous MSc dissertations and PhD theses while contributing significantly to institutional computing infrastructure. He chaired the University Computer Centre for 17 years (1985-2002), establishing Portugal's first national parallel computing service. For 11 years (2006-2017), he led the Advanced Computing theme in the University of Texas at Austin-Portugal cooperation program. Since 2005, he has directed the university's SeARCH heterogeneous computing cluster, supporting research across multiple scientific disciplines. His research group has developed several influential frameworks including JaSkel (Java Skeleton-Based Framework for Cluster and Grid Computing), HEP-Frame (for LHC data analysis), Im2Cr (crystallographic imaging tool), and CROSS-Fire (risk management decision support system). These platforms demonstrate his commitment to translating theoretical advances into practical tools that address real-world scientific and societal challenges.

• Quantum Cryptography
• Data Structures and Algorithms
• Quantum Algorithms
• +۳ مورد دیگر

Dr. Troy Lee is an Associate Professor of Quantum Cryptography at the Centre for Quantum Software and Information within the Faculty of Engineering and Information Technology at the University of Technology Sydney . His research focuses on quantum algorithms, computational complexity, and graph theory, with particular emphasis on quantum-classical separations and query complexity. Education: Not explicitly mentioned Research Areas: Quantum algorithms, computational complexity, graph theory, quantum cryptography, and Boolean function analysis Teaching: Supervised the course Data Structures and Algorithms in 2022 Grants: Currently involved in quantum algorithm design and defense optimization projects (2025-2028, 2021-2024) His recent publications highlight advancements in quantum query complexity, graph algorithms, and exact learning techniques. Notably, his work includes quantum speedups for graph connectivity problems and improved bounds for Fourier-sparse function learning. Dr. Lee maintains active collaborations across theoretical computer science and quantum computing domains, contributing to both foundational and applied research in quantum software development.

• Graph Algorithms
• Convex Optimization
• Fine-Grained Complexity Theory
• +۳ مورد دیگر

Rasmus Kyng is an Assistant Professor at the Department of Computer Science, ETH Zurich, since Fall 2019. He focuses on developing fast algorithms for graph problems, convex optimization, and structured linear equations, with a specialization in fine-grained complexity theory and dynamic graph algorithms. Assistant Professor, Department of Computer Science, ETH Zurich (2019–present) Postdoc, Theory of Computation Group, Harvard (2018–2019) Research Fellow, Simons Institute, UC Berkeley (Fall 2017) His research explores the intersection of graph algorithms, optimization, and complexity, particularly through dynamic graph data structures, sparsification, and applications in machine learning. Recent work includes breakthroughs in almost-linear time algorithms for network flow problems. Key trends in his publications include: Advancements in dynamic graph algorithms and data structures Applications of convex optimization to graph theory Connections between matrix theory and algorithmic efficiency Derandomization techniques for algorithmic speed Expander graph-based oblivious routing Sparsification and low-congestion vertex sparsifiers Scientific recognition includes the FOCS Best Paper Award (2022), ICBS Frontiers of Science Award (2022), and the Machtey Award for Best Student Paper (2017). His group mentors PhD candidates and postdocs, including Ming Ding , Federico Soldà , Simon Meierhans , Aurelio Sulser , and Wuwei Yuan . Research is supported by Swiss National Science Foundation grants (project no. 200021 204787, starting grant no. TMSGI2 218022).

• Algorithms
• Data Structures
• Dynamic Graph Algorithms
• +۳ مورد دیگر

Monika Henzinger is a Professor at the Institute of Science and Technology Austria (ISTA), where she has been serving since 2023, and additionally holds the position of Vice President for Technology Transfer since 2024. Her academic journey includes professorships at the University of Vienna (2009-2023) and EPFL in Switzerland (2005-2009), as well as industry experience at Google (1999-2005) and Digital Equipment Corporation (1996-1999). She earned her PhD from Princeton University in 1993 and served as an Assistant Professor at Cornell University from 1993-1996. Dr. Henzinger's research focuses on the design and analysis of efficient algorithms and data structures, with particular emphasis on dynamic settings where inputs change repeatedly, privacy-preserving algorithms, and translating theoretical algorithms into practical implementations. Her work bridges theoretical computer science with practical applications, addressing fundamental questions about computational efficiency in evolving data environments. Her recent publications (2024-2025) demonstrate a consistent focus on dynamic graph algorithms, differential privacy, and optimization problems. These works explore cutting-edge approaches to maintaining graph structures under continuous updates, developing privacy-preserving mechanisms for streaming data, and creating efficient approximation algorithms for fundamental graph problems. The research shows strong connections between theoretical guarantees and practical implementations, with many papers addressing both theoretical bounds and experimental validation. Dr. Henzinger has received numerous prestigious awards throughout her career, including: The 2024 Best Paper Award at the Symposium on Discrete Algorithms The 2021 Wittgenstein Award Two ERC Advanced Grants (2014, 2021) Fellow of the Association of Computing Machinery (2016) Member of the Austrian Academy of Sciences (2017) The Carus medal of the German Academy of Sciences Leopoldina (2019) She currently leads an active research group comprising PhD students and postdoctoral researchers, and her team is supported by multiple significant grants including an ERC Advanced Grant for 'The design and evaluation of modern fully dynamic data structures,' a Wittgenstein Award from the Austrian Science Fund, and several other projects focused on dynamic graph algorithms and data structures. Her collaborative work spans theoretical computer science, algorithm design, and practical implementations. Dr. Henzinger's research group operates within a vibrant ecosystem of algorithmic research at ISTA, focusing on transforming theoretical insights about computational efficiency into practical tools for handling dynamic data. The group maintains strong connections with both theoretical and applied research communities, bridging the gap between abstract algorithm design and real-world implementation challenges.

Jochen Koenemannمشاهده پروفایل

استاد

• Approximation Algorithms
• Algorithmic Game Theory
• Combinatorial Optimization

Jochen Koenemann is a Professor & Chair in the Department of Combinatorics & Optimization at the University of Waterloo. He holds a Ph.D. in Algorithms, Combinatorics & Optimization from Carnegie Mellon University (2003) and completed a postdoc at La Sapienza University of Rome (2003-2004). His research focuses on Approximation Algorithms , Algorithmic Game Theory , and Combinatorial Optimization . Notable contributions include work on stable matchings, nucleolus computation, and network design algorithms. He has organized major events like the Hausdorff Summer School and IPCO'17 in Waterloo. Academic leadership roles include chairing conferences and serving on program committees such as EC'18, APPROX'17, and SODA'14. Teaching highlights include courses on Combinatorial Optimization , Game Theory , and Algorithmic Game Theory . He co-authored an undergraduate textbook on optimization with B. Guenin and L. Tuncel. Current advisees include M. Rundstrom, H. Sun, W. Toth, and M. VanDyk. His work frequently addresses interdisciplinary challenges, such as collaborations with SickKids Hospital on algorithmic solutions for medical applications. Professional activities include editorial roles and international collaborations. Recent research trends emphasize algorithmic frameworks for game-theoretic problems and improving approximation bounds for classical combinatorial challenges. Awards and recognitions are not explicitly listed, but his extensive program committee participation underscores his academic influence.

Danupon Na Nongkaiمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Graph Algorithms
• Dynamic Computing
• +۶ مورد دیگر

Dr. Danupon Na Nongkai is an Associate Professor in the Division of Theoretical Computer Science at KTH Royal Institute of Technology's School of Electrical Engineering and Computer Science. His research focuses on theoretical computer science, with specialization in graph algorithms for dynamic and distributed environments. Supported by prestigious grants including the Swedish Research Council's VR Young Researcher Grant (2015) and the European Research Council's Starting Grant (2016), his work spans approximation algorithms, communication complexity, game theory, verification, theoretical databases, quantum algorithms, and social network analysis. PhD in Algorithms, Combinatorics, and Optimization (ACO) from Georgia Tech (2011) Co-winner of Principles of Distributed Computing Doctoral Dissertation Award (2013) Research Interests: Dr. Na Nongkai investigates fundamental problems in graph theory and algorithm design, particularly in dynamic, distributed, and quantum computing paradigms. His research connects theoretical computer science with practical applications in network processing, optimization, and complexity analysis. Article Trends: Recent publications demonstrate expertise in near-linear time algorithms for shortest paths, cut problems, and connectivity in diverse computational models. Key themes include cross-paradigm optimization (dynamic/static, distributed/parallel, quantum), expander decomposition applications, and submodular function analysis. Scientific Recognition: FOCS 2022 Best Paper Award ERC Starting Grant recipient VR Young Researcher Grant PODC Doctoral Dissertation Award Advising & Grants: As a principal investigator, Dr. Na Nongkai has mentored numerous researchers through collaborative publications with 85 co-authors. His group's funding includes competitive national and European grants for cutting-edge algorithm research.

Yanglei Songمشاهده پروفایل

استادیار

• Sequential Decision-Making
• Mathematical Statistics
• High-Dimensional Statistics
• +۷ مورد دیگر

Yanglei Song is an Assistant Professor in the Department of Mathematics and Statistics at Queen's University, affiliated with the Faculty of Arts and Science. His research focuses on sequential decision-making problems, including hypothesis testing, change detection, and multi-armed bandits, alongside mathematical statistics such as U-statistics, high-dimensional statistics, and cut-point analysis. Education: Ph.D. in Statistics, University of Illinois at Urbana-Champaign (2019) M.Sc. in Mathematics, University of Illinois at Urbana-Champaign (2016) B.Eng. in Electronic Engineering, Tsinghua University (2012) Research Interests: Sequential decision-making frameworks with applications in hypothesis testing and stochastic optimization. Statistical methodologies for high-dimensional data and nonparametric modeling. Development of robust algorithms for reinforcement learning and adaptive systems. Mathematical foundations of U-statistics and their computational guarantees. Publications Trends: Recent work emphasizes theoretical advancements in bandit algorithms, robust model approximation, and statistical inference under covariate-adaptive randomization. Contributions span machine learning, computational statistics, and applied mathematics, with a focus on practical implementations and rigorous theoretical analysis. Students: J. Liu Y. Zhou Z. Wang Z. Zhao N. Li M. Zhou Labs/Teams: Maintains an active research group focused on statistical learning and sequential decision-making, with open-source projects such as Stratified incomplete local simplex tests for nonparametric regression analysis.

• Machine Learning
• Optimization
• Reinforcement Learning
• +۴ مورد دیگر

Karan Singh is an Assistant Professor of Operations Research at the Tepper School of Business , Carnegie Mellon University. His work bridges machine learning and optimization, focusing on algorithmic solutions for interactive learning paradigms such as reinforcement learning and online control. Education : PhD in Computer Science (Princeton University, 2022), MA in Computer Science (Princeton, 2017), BTech in Computer Science (IIT Kanpur, 2015) His research addresses computational and statistical challenges in feedback-driven learning systems, with key contributions in algorithmic reductions, nonstochastic control, and privacy-preserving online optimization. Recent work includes developing sample-efficient boosting methods and establishing regret bounds for differentially private learning. Notable scientific achievements include the Porter Ogden Jacobus Fellowship (Princeton's highest graduate honor), a Best Paper Award at the OptRL workshop (NeurIPS 2019), and a Spotlight Prize at the New York Academy of Sciences' ML Symposium (2018). Karan actively contributes to academic service as a reviewer for International Conference on Machine Learning (ICML) , NeurIPS , and Mathematics of Operations Research . He serves on CMU committees including the Business Analytics Teaching Track Search Committee and Special Taskforce for Testing Requirements .

Oren Weimannمشاهده پروفایل

استاد

• Theoretical Computer Science
• Design and Analysis of Algorithms
• Data Structures
• +۹ مورد دیگر

Oren Weimann is a Professor in the Department of Computer Science at the University of Haifa, Faculty of Natural Sciences. His research lies at the intersection of theoretical computer science, algorithm design, and data structures, with a strong focus on planar graphs, combinatorial pattern matching, and fine-grained complexity. He has published extensively in top-tier venues such as STOC, SODA, ICALP, PODC, and ESA. Education: Ph.D., Massachusetts Institute of Technology (MIT), 2005–2009. Advisor: Erik Demaine. Dissertation: "Accelerating Dynamic Programming" Postdoc, Weizmann Institute of Science, 2009–2011. Host: David Peleg M.Sc., University of Haifa, 2004–2005. Advisor: Gad Landau. Dissertation: "Using PQ trees for Comparative Genomics" B.A., Technion – Israel Institute of Technology, 1999–2002 Oren Weimann's research centers on the design and analysis of efficient algorithms, particularly for planar and structured graphs. His work explores fundamental problems such as shortest paths, distance oracles, fault tolerance, edit distance, and pattern matching. He investigates both upper and lower bounds, often pushing the limits of what is computationally feasible under fine-grained complexity assumptions. His contributions include optimal labeling schemes, compressed data structures, and breakthroughs in dynamic and distributed graph algorithms. His recent publications reveal a consistent trend in developing highly efficient algorithms for planar graphs, with a focus on distance computation, fault tolerance, and compression. Keywords across these works include planar graphs, dynamic programming, string matching, and conditional lower bounds, reflecting a deep integration of algorithmic techniques and complexity theory. He frequently collaborates with leading researchers such as Shay Mozes, Paweł Gawrychowski, and Philip Bille. Scientific Awards: Best Paper Award, CPM 2007 Best Paper Award, ICALP 2020 (mentioned in context of work) Oren Weimann has advised numerous PhD and Master’s students, including Yaseen Abd-Elhaleem, Nathan Wallheimer, Aviv Bar-natan, and Shon Feller, whose dissertations have led to publications in major conferences. He has also mentored several postdoctoral researchers such as Shay Golan, Itai Boneh, and Panagiotis Charalampopoulos. His work has been supported by competitive research grants, though specific grant titles are not listed in the text. He has served on the program committees of key conferences including SODA, ICALP, CPM, ESA, and SPIRE, demonstrating active leadership in the theoretical computer science community. He is associated with a vibrant research group focused on algorithms and data structures, likely involving collaboration with students and postdocs on projects related to graph algorithms, string processing, and complexity. While no formal lab name is mentioned, his collaborative output suggests a strong, productive research team at the University of Haifa.

• Theoretical Computer Science
• Property Testing
• Approximation Algorithms
• +۸ مورد دیگر

Yuichi Yoshida is a Professor at the National Institute of Informatics (NII), affiliated with the Principles of Informatics Research Division. He also holds a concurrent position as Senior Researcher at Preferred Networks. His academic career at NII spans from Assistant Professor (2012–2015), Associate Professor (2015–2022), to full Professor since 2022. He serves as Vice Director of the Global Research Center for Big Data Mathematics at NII and has held advisory and research roles at Preferred Infrastructure and the Ministry of Education, Culture, Sports, Science and Technology (MEXT). Ph.D. in Informatics, Kyoto University, 2012 Master of Informatics, Kyoto University, 2009 Bachelor of Engineering, Kyoto University, 2007 His research focuses on theoretical computer science, particularly property testing , approximation algorithms , sublinear-time algorithms , and constraint satisfaction problems . He investigates how theoretical insights can be applied to real-world graphs, with recent emphasis on average sensitivity of algorithms and Lipschitz continuity in combinatorial optimization . His work bridges theory and practical scalability in large network analysis and machine learning. The most recent 15 publications (2022–2025) demonstrate a strong trend in algorithmic stability, spectral methods for hypergraphs, influence propagation in networks, and learning-augmented algorithms. Key venues include FOCS, SODA, ICALP, and NeurIPS, reflecting high impact in theoretical computer science and machine learning. Topics such as Lipschitz continuity, average sensitivity, spectral sparsification, and sublinear algorithms recur, indicating a cohesive research program on robust and efficient computation. Best Paper Award, AISTATS (2018) MEXT Commendation for Science and Technology – Young Scientists’ Prize (2017) Inoue Research Award for Young Scientists (2014) KDDI Foundation Award (2024) Funai Information Technology Award (2024) Yuichi Yoshida advises PhD students and hosts numerous postdocs, RAs, and international interns. He leads major research projects funded by JSPS and JST, including Grants-in-Aid for Scientific Research (S) and PRESTO programs. His service includes program committee roles for ICML, NeurIPS, SODA, and ICALP. He has co-authored a book on Property Testing and contributed to encyclopedias on big data technologies. He leads research teams on algorithm desensitization and large-scale graph algorithms, and has been program co-chair for GRADES-NDA'23. His lab fosters international collaboration, hosting interns from top universities worldwide.